Add {Manifest, Project}.toml and optimize fit-bayes.jl
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Before: ```julia 113.925800 seconds (350.11 M allocations: 112.508 GiB, 21.51% gc time, 0.01% compilation time) ``` Now: ```julia 17.208975 seconds (60.07 M allocations: 6.410 GiB, 9.11% gc time) ``` I add *.toml files so that one can just active the project and run the scripts. This addresses the issue mentioned in README.md: > The Julia examples depend only on standard packages which are part of > the Julia package ecosystem, and are therefore easy to add, in > principle. But in my limited experience, package dependency conflicts > are even more of a problem in Julia than they are in Python, and that's > saying something.
```julia julia> out = @time mcmc(beta, kern, 10000, 1000); 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 6000 6100 6200 6300 6400 6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 7600 7700 7800 7900 8000 8100 8200 8300 8400 8500 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500 9600 9700 9800 9900 10000 . 7.521123 seconds (10.00 M allocations: 1.193 GiB, 3.59% gc time) ```
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FWIW, I tend to have separate projects for everything I do. |
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To have fast code in Julia, we need to make sure that the code is type stable. In addition to make the code type stable (avoid creating closures over global variables), I also added a buffer to minimize allocations and used
LoopVectorizationto acceleratelogevaluation.Before:
Now:
I add *.toml files so that one can just active the project and run the
scripts. This addresses the issue mentioned in README.md: